jgtagentic

Project Roadmap: Trader Intent to JGTML Spec

This document outlines the development roadmap for the Trader Intent to JGTML Spec application.

User-Centric Design: Focus on creating an intuitive and efficient experience for traders.
Modularity: Build components and services that are well-defined and can be independently developed and tested.
Accuracy & Reliability: Strive for high accuracy in LLM translations and robust error handling.
Extensibility: Design the system to be adaptable for future JGTML spec versions and integrations.

[✅] Trader Intent Input UI:
- Text area for narrative input.
- Example narrative button/dropdown.
- Reset functionality.
[✅] LLM Translation Service:
- Integration with Google Gemini API (gemini-2.5-flash-preview-04-17).
- Prompt engineering to guide JSON output based on JGTMLSpec TypeScript interface.
- Basic response cleaning (removing markdown fences).
[✅] JGTML Spec Display:
- Formatted JSON display of the LLM-generated spec.
[✅] Simulated IntentSpecParser (Interpreter):
- Simulate parsing logic with success/error states.
- Display simulated parsed output/preview.
[✅] UI/UX Shell:
- Responsive design with Tailwind CSS.
- Visual flow representation using FlowCard components.
- Loading states and basic error messaging for each step.
[✅] TypeScript Implementation: Strong typing for data structures and component props.
[✅] API Key Management: Secure handling of API key via environment variables.
[✅] Project Documentation: Initial README.md & FLOW.md.
[✅] Trading Narrative Assistant (Chat):
- Integrated chat interface to help users formulate their narrative.
- Ability to use chat summary to populate the main narrative input.
- Speech-to-text and multimodal input support (images, audio).
[✅] Copy to Clipboard: For generated JSON outputs.
[✅] Multiple Example Narratives: Loaded from a data file and selectable via a dropdown.

Improved Error Handling & User Feedback:
- More granular error messages from the Gemini API and parser.
- Visual cues for specific errors (e.g., highlighting problematic parts of the spec if identifiable).
- User guidance on how to resolve common issues (e.g., rephrasing narrative, checking API key).
Advanced JGTML Spec Features:
- Support for more complex signal definitions, conditions (e.g., AND/OR logic), and parameters within the spec.
- Investigate LLM’s ability to infer nested structures or arrays of conditions.
LLM Prompt Engineering & Iteration:
- Continuous refinement of the prompt sent to the LLM for better accuracy, consistency, and handling of edge cases.
- Few-shot prompting examples within the main prompt if beneficial.
- Explore systemInstruction capabilities more deeply.
JGTML Spec Editor (Optional - UI Enhancement):
- Allow users to manually edit/refine the LLM-generated JGTML spec directly in the UI before parsing.
- Syntax highlighting for the JSON editor.
State Management Review:
- Evaluate if a more robust state management solution (e.g., Zustand, Jotai, or React Context improvements) is needed as complexity grows.
Component Reusability:
- Further abstract common UI patterns or logic into reusable hooks or components.
Enhanced Loading/Skeleton States:
- More polished loading indicators, possibly skeleton screens for a better UX during API calls.

Actual IntentSpecParser (Interpreter) Implementation:
- Develop a Python (or other backend) service that rigorously validates and parses the JGTML spec according to defined rules.
- API endpoint for the frontend to send the spec and receive detailed parsing results.
JGTML Execution Core:
- Implement the Python modules (e.g., run_spec() executor, indicator loaders, signal validator, decision node) as described.
- Develop a system to execute these modules based on the parsed spec.
CampaignLauncher Service:
- Create a service that takes the processed signal package and generates executable trading campaign scripts or API actions.
Database Integration (Echo Lattice):
- Design and implement a database schema to store:
  - Trader narratives.
  - Generated JGTML specs.
  - Parsing results.
  - Signal processing outcomes (from Execution Core).
  - Campaign execution details and results.
  - User feedback on strategy performance.
- API endpoints for logging and retrieving this data.

LLM Fine-tuning / Reinforcement Learning from Human Feedback (RLHF):
- Use data from the “Echo Lattice” (especially feedback on successful/failed translations and trade outcomes) to fine-tune a custom LLM or improve prompting strategies for the Gemini API.
Conceptual Exploration: Defining JGTML as a Trading Meta-Language:
- Investigate the potential to expand JGTML beyond its current structural definition into a more comprehensive “Trading Meta Language.” This involves exploring standardized vocabularies and grammars for traders to express complex strategies and analyses, aiming for a richer, more nuanced transformation from human intent to machine execution. Could JGTML become a formal bridge between qualitative insight and quantitative action?
Version Control for JGTML Specs:
- Allow saving, loading, and versioning of trader narratives and their corresponding specs.
Multi-User Support & Collaboration (If applicable):
- User authentication and authorization.
- Shared repositories of strategies or specs.
Direct Broker/Exchange Integration:
- Securely connect to trading platforms to execute generated scripts/campaigns.
Advanced Analytics & Reporting:
- Dashboard to visualize the performance of strategies derived from JGTML specs.
- Analysis of LLM translation accuracy over time.
Chart Integration:
- Display relevant charts alongside the narrative input or spec generation for better context.

This roadmap is a living document and will evolve as the project progresses and new requirements or ideas emerge.